Researcher-Machine Learning at McMahon Lab, Cornell University
New York, New York, United States
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Summary
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Maxwell Anderson is a machine learning researcher and software engineer with a decade of hands-on experience building research-driven systems and production software from New York. At Cornell’s McMahon Lab he studies nonlinear dynamics in deep nets, prototypes photonic neural networks, and simulates reservoir computing and ESN hardware in PyTorch, blending physics-informed engineering with ML modeling. He also architects full-stack ground station software for an Alpha CubeSat using Clojure/Clojurescript and ELK integrations, demonstrating a rare mix of embedded, back-end, and observability expertise. An active open-source Rust contributor—describing himself as a “100% oxidized Rustacean”—he has improved linting and code-analysis in the widely used rust-clippy project. Maxwell’s background in Engineering Physics and Computer Science (Cornell, 3.98 GPA) and internships across cybersecurity, big-data DSLs, and finance give him a strong foundation in systems thinking and practical deployment. He excels at translating theoretical research into efficient, deployable systems across hardware and software stacks.
10 years of coding experience
1 year of employment as a software developer
Bachelor of Science - (expected graduation date: 12/2020), Engineering Physics and Computer Science (double major), GPA: 3.98, Bachelor of Science - (expected graduation date: 12/2020), Engineering Physics and Computer Science (double major), GPA: 3.98 at Cornell University College of Engineering
GPA: 95 (out of 100), GPA: 95 (out of 100) at Stuyvesant High School
A bunch of lints to catch common mistakes and improve your Rust code. Book: https://doc.rust-lang.org/clippy/
Role in this project:
Back-end Developer
Contributions:7 PRs, 7 comments, 1 issue in 2 months
Contributions summary:Maxwell primarily contributed to the `rust-clippy` project, focusing on linting and code analysis improvements for Rust code. Their work involved modifications to existing lints, the addition of new checks related to literal representations, and enhancements to the codebase for better accuracy. They also integrated changes from upstream branches and addressed minor code formatting issues.
Contributions:10 commits, 10 pushes, 3 branches in 3 years 3 months
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Maxwell Anderson - Researcher-Machine Learning at McMahon Lab, Cornell University